



"""simple docstring"""






import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402



class     UpperCAmelCase    (	unittest.TestCase  ):



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      0



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''				)
     self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     with tempfile.TemporaryDirectory() as tmpdirname:
       _snake_case   	=      Path(__lowerCamelCase				) / '''preprocessor_config.json'''
       _snake_case   	=      Path(__lowerCamelCase				) / '''config.json'''
       json.dump(
           {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}							,       open(__lowerCamelCase							,       '''w'''				)							,       )
       json.dump({'''model_type''': '''clip'''}							,       open(__lowerCamelCase							,       '''w'''				)				)

       _snake_case   	=      AutoImageProcessor.from_pretrained(__lowerCamelCase				)
       self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     # Ensure we can load the image processor from the feature extractor config
     with tempfile.TemporaryDirectory() as tmpdirname:
       _snake_case   	=      Path(__lowerCamelCase				) / '''preprocessor_config.json'''
       _snake_case   	=      Path(__lowerCamelCase				) / '''config.json'''
       json.dump(
           {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}							,       open(__lowerCamelCase							,       '''w'''				)							,       )
       json.dump({'''model_type''': '''clip'''}							,       open(__lowerCamelCase							,       '''w'''				)				)

       _snake_case   	=      AutoImageProcessor.from_pretrained(__lowerCamelCase				)
       self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     with tempfile.TemporaryDirectory() as tmpdirname:
       _snake_case   	=      CLIPConfig()

       # Create a dummy config file with image_proceesor_type
       _snake_case   	=      Path(__lowerCamelCase				) / '''preprocessor_config.json'''
       _snake_case   	=      Path(__lowerCamelCase				) / '''config.json'''
       json.dump(
           {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}							,       open(__lowerCamelCase							,       '''w'''				)							,       )
       json.dump({'''model_type''': '''clip'''}							,       open(__lowerCamelCase							,       '''w'''				)				)

       # remove image_processor_type to make sure config.json alone is enough to load image processor locally
       _snake_case   	=      AutoImageProcessor.from_pretrained(__lowerCamelCase				).to_dict()

       config_dict.pop('''image_processor_type'''				)
       _snake_case   	=      CLIPImageProcessor(**__lowerCamelCase				)

       # save in new folder
       model_config.save_pretrained(__lowerCamelCase				)
       config.save_pretrained(__lowerCamelCase				)

       _snake_case   	=      AutoImageProcessor.from_pretrained(__lowerCamelCase				)

       # make sure private variable is not incorrectly saved
       _snake_case   	=      json.loads(config.to_json_string()				)
       self.assertTrue('''_processor_class''' not in dict_as_saved				)

     self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     with tempfile.TemporaryDirectory() as tmpdirname:
       _snake_case   	=      Path(__lowerCamelCase				) / '''preprocessor_config.json'''
       json.dump(
           {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}							,       open(__lowerCamelCase							,       '''w'''				)							,       )

       _snake_case   	=      AutoImageProcessor.from_pretrained(__lowerCamelCase				)
       self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     with self.assertRaisesRegex(
         __lowerCamelCase							,       '''clip-base is not a local folder and is not a valid model identifier'''				):
       _snake_case   	=      AutoImageProcessor.from_pretrained('''clip-base'''				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     with self.assertRaisesRegex(
         __lowerCamelCase							,       R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''				):
       _snake_case   	=      AutoImageProcessor.from_pretrained(__lowerCamelCase							,       revision='''aaaaaa'''				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     with self.assertRaisesRegex(
         __lowerCamelCase							,       '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''							,       ):
       _snake_case   	=      AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     # If remote code is not set, we will time out when asking whether to load the model.
     with self.assertRaises(__lowerCamelCase				):
       _snake_case   	=      AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''				)
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(__lowerCamelCase				):
       _snake_case   	=      AutoImageProcessor.from_pretrained(
           '''hf-internal-testing/test_dynamic_image_processor'''							,       trust_remote_code=__lowerCamelCase				)

     _snake_case   	=      AutoImageProcessor.from_pretrained(
         '''hf-internal-testing/test_dynamic_image_processor'''							,       trust_remote_code=__lowerCamelCase				)
     self.assertEqual(image_processor.__class__.__name__							,       '''NewImageProcessor'''				)

     # Test image processor can be reloaded.
     with tempfile.TemporaryDirectory() as tmp_dir:
       image_processor.save_pretrained(__lowerCamelCase				)
       _snake_case   	=      AutoImageProcessor.from_pretrained(__lowerCamelCase							,       trust_remote_code=__lowerCamelCase				)
     self.assertEqual(reloaded_image_processor.__class__.__name__							,       '''NewImageProcessor'''				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     try:
       AutoConfig.register('''custom'''							,       __lowerCamelCase				)
       AutoImageProcessor.register(__lowerCamelCase							,       __lowerCamelCase				)
       # Trying to register something existing in the Transformers library will raise an error
       with self.assertRaises(__lowerCamelCase				):
         AutoImageProcessor.register(__lowerCamelCase							,       __lowerCamelCase				)

       with tempfile.TemporaryDirectory() as tmpdirname:
         _snake_case   	=      Path(__lowerCamelCase				) / '''preprocessor_config.json'''
         _snake_case   	=      Path(__lowerCamelCase				) / '''config.json'''
         json.dump(
             {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}							,       open(__lowerCamelCase							,       '''w'''				)							,       )
         json.dump({'''model_type''': '''clip'''}							,       open(__lowerCamelCase							,       '''w'''				)				)

         _snake_case   	=      CustomImageProcessor.from_pretrained(__lowerCamelCase				)

       # Now that the config is registered, it can be used as any other config with the auto-API
       with tempfile.TemporaryDirectory() as tmp_dir:
         image_processor.save_pretrained(__lowerCamelCase				)
         _snake_case   	=      AutoImageProcessor.from_pretrained(__lowerCamelCase				)
         self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)

     finally:
       if "custom" in CONFIG_MAPPING._extra_content:
         del CONFIG_MAPPING._extra_content["custom"]
       if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
         del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""



     class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
        A__		:	int           =  True

     try:
       AutoConfig.register('''custom'''							,       __lowerCamelCase				)
       AutoImageProcessor.register(__lowerCamelCase							,       __lowerCamelCase				)
       # If remote code is not set, the default is to use local
       _snake_case   	=      AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''				)
       self.assertEqual(image_processor.__class__.__name__							,       '''NewImageProcessor'''				)
       self.assertTrue(image_processor.is_local				)

       # If remote code is disabled, we load the local one.
       _snake_case   	=      AutoImageProcessor.from_pretrained(
           '''hf-internal-testing/test_dynamic_image_processor'''							,       trust_remote_code=__lowerCamelCase				)
       self.assertEqual(image_processor.__class__.__name__							,       '''NewImageProcessor'''				)
       self.assertTrue(image_processor.is_local				)

       # If remote is enabled, we load from the Hub
       _snake_case   	=      AutoImageProcessor.from_pretrained(
           '''hf-internal-testing/test_dynamic_image_processor'''							,       trust_remote_code=__lowerCamelCase				)
       self.assertEqual(image_processor.__class__.__name__							,       '''NewImageProcessor'''				)
       self.assertTrue(not hasattr(__lowerCamelCase							,       '''is_local'''				)				)

     finally:
       if "custom" in CONFIG_MAPPING._extra_content:
         del CONFIG_MAPPING._extra_content["custom"]
       if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
         del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]










'''simple docstring'''



import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_  :							Optional[int]             =    logging.get_logger(__name__)
def        UpperCAmelCase (    A	:					str					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      SwinConfig.from_pretrained(
							    '''microsoft/swin-tiny-patch4-window7-224'''       ,		out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']					)
							SCREAMING_SNAKE_CASE       :   List[str]         =      MaskFormerConfig(backbone_config=A					)

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      '''huggingface/label-files'''
							if "ade20k-full" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      847
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''maskformer-ade20k-full-id2label.json'''
							elif "ade" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   List[str]         =      150
														SCREAMING_SNAKE_CASE       :   Any         =      '''ade20k-id2label.json'''
							elif "coco-stuff" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      171
														SCREAMING_SNAKE_CASE       :   List[Any]         =      '''maskformer-coco-stuff-id2label.json'''
							elif "coco" in model_name:
														# TODO
														SCREAMING_SNAKE_CASE       :   str         =      133
														SCREAMING_SNAKE_CASE       :   Any         =      '''coco-panoptic-id2label.json'''
							elif "cityscapes" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      19
														SCREAMING_SNAKE_CASE       :   List[Any]         =      '''cityscapes-id2label.json'''
							elif "vistas" in model_name:
														# this should be ok
														SCREAMING_SNAKE_CASE       :   Tuple         =      65
														SCREAMING_SNAKE_CASE       :   Tuple         =      '''mapillary-vistas-id2label.json'''

							SCREAMING_SNAKE_CASE       :   List[str]         =      json.load(open(hf_hub_download(A       ,		A       ,		repo_type='''dataset'''					)       ,		'''r'''					)					)
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {int(A					): v for k, v in idalabel.items()}

							return config
def        UpperCAmelCase (    A	:					Union[str, Any]					):
							SCREAMING_SNAKE_CASE       :   Dict         =      []
							# stem
							# fmt: off
							rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')					)
							rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')					)
							rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')					)
							rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')					)
							# stages
							for i in range(len(config.backbone_config.depths					)					):
														for j in range(config.backbone_config.depths[i]					):
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")					)

														if i < 3:
																					rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")					)
																					rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")					)
														rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")					)
														rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")					)

							# FPN
							rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')					)
							rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')					)
							rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')					)
							for source_index, target_index in zip(range(3       ,		0       ,		-1					)       ,		range(0       ,		3					)					):
														rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")					)
														rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")					)
														rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")					)
														rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")					)
														rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")					)
														rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")					)
							rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')					)
							rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')					)

							# Transformer decoder
							for idx in range(config.decoder_config.decoder_layers					):
														# self-attention out projection
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")					)
														# cross-attention out projection
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")					)
														# MLP 1
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")					)
														# MLP 2
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")					)
														# layernorm 1 (self-attention layernorm)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")					)
														# layernorm 2 (cross-attention layernorm)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")					)
														# layernorm 3 (final layernorm)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")					)

							rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')					)
							rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')					)

							# heads on top
							rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')					)

							rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')					)
							rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')					)

							rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')					)
							rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')					)

							for i in range(3					):
														rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""")					)
														rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""")					)
							# fmt: on

							return rename_keys
def        UpperCAmelCase (    A	:					Union[str, Any]       ,		A	:					Tuple       ,		A	:					int					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      dct.pop(A					)
							SCREAMING_SNAKE_CASE       :   List[str]         =      val
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					List[Any]					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      [int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
							for i in range(len(backbone_config.depths					)					):
														SCREAMING_SNAKE_CASE       :   List[str]         =      num_features[i]
														for j in range(backbone_config.depths[i]					):
																					# fmt: off
																					# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""					)
																					SCREAMING_SNAKE_CASE       :   Any         =      state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""					)
																					# next, add query, keys and values (in that order) to the state dict
																					SCREAMING_SNAKE_CASE       :   int         =      in_proj_weight[:dim, :]
																					SCREAMING_SNAKE_CASE       :   Tuple         =      in_proj_bias[: dim]
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      in_proj_weight[
																					    dim : dim * 2, :
																					]
																					SCREAMING_SNAKE_CASE       :   str         =      in_proj_bias[
																					    dim : dim * 2
																					]
																					SCREAMING_SNAKE_CASE       :   Any         =      in_proj_weight[
																					    -dim :, :
																					]
																					SCREAMING_SNAKE_CASE       :   str         =      in_proj_bias[-dim :]
																					# fmt: on
def        UpperCAmelCase (    A	:					Any       ,		A	:					Optional[Any]					):
							# fmt: off
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      config.decoder_config.hidden_size
							for idx in range(config.decoder_config.decoder_layers					):
														# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""					)
														SCREAMING_SNAKE_CASE       :   List[str]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""					)
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE       :   Any         =      in_proj_weight[: hidden_size, :]
														SCREAMING_SNAKE_CASE       :   List[Any]         =      in_proj_bias[:config.hidden_size]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      in_proj_weight[hidden_size : hidden_size * 2, :]
														SCREAMING_SNAKE_CASE       :   int         =      in_proj_bias[hidden_size : hidden_size * 2]
														SCREAMING_SNAKE_CASE       :   Any         =      in_proj_weight[-hidden_size :, :]
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      in_proj_bias[-hidden_size :]
														# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE       :   List[Any]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""					)
														SCREAMING_SNAKE_CASE       :   List[Any]         =      state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""					)
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE       :   List[Any]         =      in_proj_weight[: hidden_size, :]
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      in_proj_bias[:config.hidden_size]
														SCREAMING_SNAKE_CASE       :   Dict         =      in_proj_weight[hidden_size : hidden_size * 2, :]
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      in_proj_bias[hidden_size : hidden_size * 2]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      in_proj_weight[-hidden_size :, :]
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      in_proj_bias[-hidden_size :]
							# fmt: on
def        UpperCAmelCase (    ):
							SCREAMING_SNAKE_CASE       :   Tuple         =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							SCREAMING_SNAKE_CASE       :   Tuple         =      Image.open(requests.get(A       ,		stream=A					).raw					)
							return im
@torch.no_grad()
def        UpperCAmelCase (    A	:					str       ,		A	:					str       ,		A	:					str       ,		A	:					bool = False					):
							SCREAMING_SNAKE_CASE       :   int         =      get_maskformer_config(A					)

							# load original state_dict
							with open(A       ,		'''rb'''					) as f:
														SCREAMING_SNAKE_CASE       :   str         =      pickle.load(A					)
							SCREAMING_SNAKE_CASE       :   List[Any]         =      data['''model''']

							# for name, param in state_dict.items():
							#     print(name, param.shape)

							# rename keys
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      create_rename_keys(A					)
							for src, dest in rename_keys:
														rename_key(A       ,		A       ,		A					)
							read_in_swin_q_k_v(A       ,		config.backbone_config					)
							read_in_decoder_q_k_v(A       ,		A					)

							# update to torch tensors
							for key, value in state_dict.items():
														SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.from_numpy(A					)

							# load 🤗 model
							SCREAMING_SNAKE_CASE       :   Any         =      MaskFormerForInstanceSegmentation(A					)
							model.eval()

							for name, param in model.named_parameters():
														print(A       ,		param.shape					)

							SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      model.load_state_dict(A       ,		strict=A					)
							assert missing_keys == [
							    "model.pixel_level_module.encoder.model.layernorm.weight",
							    "model.pixel_level_module.encoder.model.layernorm.bias",
							]
							assert len(A					) == 0, F"""Unexpected keys: {unexpected_keys}"""

							# verify results
							SCREAMING_SNAKE_CASE       :   Tuple         =      prepare_img()
							if "vistas" in model_name:
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      65
							elif "cityscapes" in model_name:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      65535
							else:
														SCREAMING_SNAKE_CASE       :   List[str]         =      255
							SCREAMING_SNAKE_CASE       :   Dict         =      True if '''ade''' in model_name else False
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      MaskFormerImageProcessor(ignore_index=A       ,		reduce_labels=A					)

							SCREAMING_SNAKE_CASE       :   List[str]         =      image_processor(A       ,		return_tensors='''pt'''					)

							SCREAMING_SNAKE_CASE       :   List[Any]         =      model(**A					)

							print('''Logits:'''       ,		outputs.class_queries_logits[0, :3, :3]					)

							if model_name == "maskformer-swin-tiny-ade":
														SCREAMING_SNAKE_CASE       :   Any         =      torch.tensor(
														    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]					)
							assert torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,		A       ,		atol=1e-4					)
							print('''Looks ok!'''					)

							if pytorch_dump_folder_path is not None:
														print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""					)
														Path(A					).mkdir(exist_ok=A					)
														model.save_pretrained(A					)
														image_processor.save_pretrained(A					)

							if push_to_hub:
														print('''Pushing model and image processor to the hub...'''					)
														model.push_to_hub(F"""nielsr/{model_name}"""					)
														image_processor.push_to_hub(F"""nielsr/{model_name}"""					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Optional[int]             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='maskformer-swin-tiny-ade',
							    type=str,
							    help=('Name of the MaskFormer model you\'d like to convert',),
							)
							parser.add_argument(
							    '--checkpoint_path',
							    default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
							    type=str,
							    help='Path to the original state dict (.pth file).',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)
							parser.add_argument(
							    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
							)

							lowerCAmelCase_  :							List[Any]             =    parser.parse_args()
							convert_maskformer_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)




